A simple analytical and experimental procedure for selection of reference genes for reverse-transcription quantitative PCR normalization data

Variation in cellular activity in a tissue induces changes in RNA concentration, which affects the validity of gene mRNA abundance analyzed by reverse transcription quantitative PCR (RT-qPCR). A common way of accounting for such variation consists of the use of reference genes for normalization. Programs such as geNorm may be used to select suitable reference genes, although a large set of genes that are not co-regulated must be analyzed to obtain accurate results. The objective of this study was to propose an alternative experimental and analytical protocol to assess the invariance of reference genes in porcine mammary tissue using mammary RNA and DNA concentrations as correction factors. Mammary glands were biopsied from 4 sows on d 110 of gestation (prepartum), on d 5 (early) and 17 (peak) of lactation, and on d 5 after weaning (postweaning). Relative expression of 7 potential reference genes, API5, MRPL39, VAPB, ACTB, GAPDH, RPS23, and MTG1, and one candidate gene, SLC7A1, was quantified by RT-qPCR using a relative standard curve approach. Variation in gene expression levels, measured as cycles to threshold at each stage of mammary physiological activity, was tested using a linear mixed model fitting RNA and DNA concentrations as covariates. Results were compared with those obtained with geNorm analysis, and genes selected by each method were used to normalize SLC7A1. Quantified relative mRNA abundance of GAPDH and MRPL39 remained unchanged across stages of mammary physiological activity after accounting for changes in tissue RNA and DNA concentration. In contrast, geNorm analysis selected MTG1, MRPL39, and VAPB as the best reference genes. However, when target gene SLC7A1 was normalized with genes selected either based on our proposed protocol or by geNorm, fold changes in mRNA abundance did not differ. In conclusion, the proposed analytical protocol assesses expression invariance of potential reference genes by accounting for variation in tissue RNA and DNA concentrations and thus represents an alternative method to select suitable reference genes for RT-qPCR analysis.